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BACKGROUND AND PURPOSE: Microstructural white matter abnormalities on DTI using Tract-Based Spatial Statistics at term-equivalent age are associated with cognitive and motor outcomes at 2 years of age or younger. However, neurodevelopmental tests administered at such early time points are insufficiently predictive of mild-moderate motor and cognitive impairment at school age. Our objective was to evaluate the microstructural antecedents of cognitive and motor outcomes at 3 years' corrected age in a cohort of very preterm infants. MATERIALS AND METHODS: We prospectively recruited 101 very preterm infants (

Many plant-based milks lack key micronutrients found in bovine milk, such as calcium and vitamin D. In this study, we fortified almond milk with these two micronutrients and used a standardized gastrointestinal model to examine the impact of product formulation on their bioaccessibility. The impact of different forms (CaCl2 versus CaCO3) and concentrations (0, 1, or 2 g per 240 mL) of calcium on the physicochemical properties, lipid digestibility, and vitamin D bioaccessibility was examined. Soluble calcium (CaCl2) promoted particle aggregation by reducing the electrostatic repulsion, while colloidal calcium (CaCO3) did not because there were fewer free calcium ions. High levels of calcium (soluble or insoluble) reduced vitamin D bioaccessibility, which was attributed to insoluble calcium soap formation in the small intestine. Calcium bioaccessibility was higher for CaCO3 than CaCl2. These findings are useful for the development of nutritionally fortified plant-based milks with improved physicochemical and nutritional properties.

Eighty nine Artemisia samples treated with different concentrations of colchicine were used as breeding samples, with diploid Artemisia as the control. The ploidy levels of samples were determined by flow cytometry and confocal laser scanning microscopy (CLSM). An analysis of the flow cytometry results identified three suspected tetraploid plants and seven suspected triploid plants. The results of this study may be useful for breeding new Artemisia lines.

This study aimed to evaluate the efficacy of cinobufotalin combined with gefitinib in the treatment of lung cancer. A549 cells were treated with gefitinib, cinobufotalin, or cinobufotalin plus gefitinib. MTT assay, annexin-V/PI staining and flow cytometry, TUNEL staining, DCFH-DA staining, Western blot, and real-time RT-PCR were performed to investigate the synergistic inhibitory effect of cinobufotalin combined with gefitinib on the growth of A549 cells. Results showed that cinobufotalin synergized with gefitinib displayed inhibited cell viability and enhanced apoptosis in the combination group. Cinobufotalin combined with gefitinib induced a significant enhancement in reactive oxygen species (ROS) production accompanied by cell cycle arrest in the S phase arrest, characterized by upregulation of p21 and downregulation of cyclin A, cyclin E, and CDK2. Besides, cinobufotalin plus gefitinib downregulated the levels of HGF and c-Met. In summary, cinobufotalin combined with gefitinib impedes viability and facilitates apoptosis of A549 cells, indicating that the combined therapy might be a new promising treatment for lung cancer patients who are resistant to gefitinib.

Parasitic loranthus [Taxillus chinensis (DC.) Danser] is an important medicinal plant that produces recalcitrant seeds that are sensitive to dehydration. Desiccation tolerance is critical for the survival of recalcitrant seeds in low-moisture environments. Clarifying how these seeds respond to desiccation is important for long-term conservation. Thus, the viability, germination, microstructure, and antioxidant enzyme activities of dehydrated parasitic loranthus seeds were investigated. Diverse organelles were degraded or deformed during dehydration treatments. Additionally, superoxide dismutase and catalase activities gradually decreased in response to desiccation stress. A proteomic analysis involving TMT-labeling and LC–MS/MS were performed. A total of 1479 proteins were identified, of which 141 were differentially expressed proteins (DEPs) at 16 and 36 h after initiating the dehydration treatments. A functional annotation based on gene ontology revealed that the DEPs were mainly localized in chloroplasts and were related to energy metabolism, responses to stimuli, and the regulation of biological processes. A KEGG pathway enrichment analysis determined that several of the identified proteins were associated with signal transductions, photosynthesis, and glycolysis/gluconeogenesis. The results suggest that the efficient removal of excessive ROS amounts may be crucial for promoting parasitic loranthus seed germination under dehydration stress conditions. A series of candidate dehydration stress-related proteins were identified and may be relevant for enhancing the dehydration tolerance of the recalcitrant seeds. To the best of our knowledge, this is the first study to elucidate the possible molecular mechanisms underlying the sensitivity of recalcitrant parasitic loranthus seeds to dehydration via a proteomic analysis involving TMT-labeling.

New avibactam derivatives in which amide moiety is replaced by substituted amidine groupings were obtained in several steps, the key step having involved the conversion of 2-positioned cyano group of the 1,6-diazabicyclo[3.2.1]-octane framework into the amidine one. Synergistic antibacterial activity of the new compounds in combination with meropenem infers the agonistic effect of these derivatives against β-lactamases.

Background The impact of prenatal opioid exposure on brain development remains poorly understood. Methods We conducted a prospective study of term-born infants with and without prenatal opioid exposure. Structural brain MRI was performed between 40 and 48 weeks postmenstrual age. T2-weighted images were processed using the Developing Human Connectome Project structural pipeline. We compared 63 relative regional brain volumes between groups. Results Twenty-nine infants with prenatal opioid exposure and 42 unexposed controls were included. The groups had similar demographics, except exposed infants had lower birth weights, more maternal smoking and maternal Hepatitis C, fewer mothers with a college degree, and were more likely non-Hispanic White. After controlling for sex, postmenstrual age at scan, birth weight, and maternal education, exposed infants had significantly smaller relative volumes of the deep gray matter, bilateral thalamic ventrolateral nuclei, bilateral insular white matter, bilateral subthalamic nuclei, brainstem, and cerebrospinal fluid. Exposed infants had larger relative volumes of the right cingulate gyrus white matter and left occipital lobe white matter. Conclusions Infants with prenatal opioid exposure had smaller brain volumes in multiple regions compared to controls, with two regions larger in the opioid-exposed group. Further research should focus on the relative contributions of maternal opioids and other exposures. Impact Prenatal opioid exposure is associated with developmental and behavioral consequences, but the direct effects of opioids on the developing human brain are poorly understood. Prior small studies using MRI have shown smaller regional brain volumes in opioid-exposed infants and children. After controlling for covariates, infants with prenatal opioid exposure scanned at 40-48 weeks postmenstrual age had smaller brain volumes in multiple regions compared to controls, with two regions larger in the opioid-exposed group. This adds to the literature showing potential impact of prenatal opioid exposure on the developing brain.

Background We sought to determine the mediating effects of magnetic resonance imaging (MRI) biomarkers at term gestation on the relationship between perinatal illness severity and neurodevelopment. Methods The Clinical Risk Index for Babies-second edition (CRIB-II) was correlated with indices of brain maturation or injury and neurodevelopment at 2-year follow-up in infants born less than 32 weeks gestation. Using a counterfactual mediation analysis, associations between CRIB-II, MRI biomarkers, and neurodevelopment were confirmed, followed by an assessment of the mediating effects of MRI biomarkers on the relationship between CRIB-II and neurodevelopment. Results CRIB-II correlated significantly with neurodevelopment and MRI biomarkers of brain injury or cortical maturation. Two MRI biomarkers, cortical surface area and global injury score, were associated with neurodevelopmental scores at follow-up and included in mediation analyses. Conclusion Biomarkers of cortical maturation or brain injury at term-equivalent age mediated a substantial portion of the risks conveyed by perinatal illness severity on neurodevelopmental outcomes at 2 years corrected age.

Our objective was to evaluate the independent prognostic value of a novel MRI biomarker—objectively diagnosed diffuse white matter abnormality volume (DWMA; diffuse excessive high signal intensity)—for prediction of motor outcomes in very preterm infants. We prospectively enrolled a geographically-based cohort of very preterm infants without severe brain injury and born before 32 weeks gestational age. Structural brain MRI was obtained at term-equivalent age and DWMA volume was objectively quantified using a published validated algorithm. These results were compared with visually classified DWMA. We used multivariable linear regression to assess the value of DWMA volume, independent of known predictors, to predict motor development as assessed using the Bayley Scales of Infant & Toddler Development, Third Edition at 3 years of age. The mean (SD) gestational age of the cohort was 28.3 (2.4) weeks. In multivariable analyses, controlling for gestational age, sex, and abnormality on structural MRI, DWMA volume was an independent prognostic biomarker of Bayley Motor scores ($$\beta $$ β = −12.59 [95% CI −18.70, −6.48] R2 = 0.41). Conversely, visually classified DWMA was not predictive of motor development. In conclusion, objectively quantified DWMA is an independent prognostic biomarker of long-term motor development in very preterm infants and warrants further study.

Purpose: In type 2 diabetes, palmitic acid could damage vessels and induce insulin resistance. This present in vitro study evaluates the possible role of Spatholobus suberectus (FSS) extract in diabetes.Methods: Human HUVECc cells were treated with palmitic acid, palmitic acid and Spatholobus suberectus extract. MALAT1 overexpression plasmid (pcDNA-MALAT1) and blank vector were transfected into the cells using lipofectamine 2000. RT-qPCR assay was used to evaluated the expression changes of lncRNA, VEGFR2 and VEGFA in the cells as well as Epithelial-Mesenchymal Transition (EMT) biomarkers and apoptosis. CCK-8 was used to detect cell viabilities of HUVECs. Expressions of proteins in VEGF signaling pathway were analyzed using Western Blot.Results: LncRNA MALAT1 had high expression in diabetes-like cells and suppressed proliferation and EMT but promoted apoptosis. The SS extract promoted proliferation and EMT and repressed apoptosis in diabetes-like HUVECs cells. The promotion of apoptosis by LncRNA MALAT1, inhibition of apoptosis and regulated functions of diabetes-like HUVECs cells by SS extract occurred via the VEGF signaling pathwayConclusion: SS extract might contribute to survival of cells by inhibiting MALAT1 via VEGF signaling pathway in vitro, suggesting FSF might be a potential therapeutic agent in the treatment of diabetes. Keywords: flavone of Spatholobus suberectus, diabetes, vascular endothelial cell, LncRNA MALAT1

The popular use of silver nanoparticles (Ag NPs) in the production of commercial odor-control and antibacterial textile products has raised questions about their washing durability. Poor durability not only deteriorates product performance but also results in unknown amounts of Ag NPs leaching into sewage- and water-treatment systems. Therefore, it is necessary to have a quick and easy method for detecting Ag NPs in washing solutions for assessment of washing durability. In this study, we have developed a practical surface-enhanced Raman spectroscopy (SERS) method for measuring the concentrations of Ag NPs in water and a detergent solution. To improve the sensitivity and reproducibility of SERS signals from the complexation of an indicator molecule (ferric dimethyl-dithiocarbamate, in this study) with NPs, the “coffee ring effect” was utilized. The active SERS “hot spots” in the aggregated NPs along the coffee ring effectively intensified the signature SERS response, even with NPs of about 10 nm in diameter and a concentration as low as 0.01 mg L−1. The linear relationships between SERS intensity and Ag NP concentration (R2 > 0.99) successfully quantified the amount of Ag NPs released from Ag NP-treated cotton fabrics during washing as well as other Ag speciation formed in a detergent solution.

Overuse and misuse of antibiotics have resulted in wide antibiotic resistance in bacteria. In this study, surface-enhanced Raman spectroscopy (SERS) was used to rapidly characterize the responses of lactic acid bacteria (LAB) to antibiotics targeting the bacterial cell wall. Lactobacillus delbrueckii subsp. bulgaricus (Lb. bulgaricus) ATCC 11842 was used to represent LAB strains that are widely used in the food industry. Penicillin G, ampicillin and vancomycin were used to treat Lb. bulgaricus ATCC 11842 at different toxic levels for 30, 60 and 90 min, respectively. SERS spectra of Lb. bulgaricus after each treatment were collected to characterize the responses of Lb. bulgaricus. The collected spectra were analyzed by principal component analysis (PCA) and partial least squares regression (PLSR). Results suggested that all three antibiotics induced significant peak changes in LAB, and the spectral changes induced by each antibiotic treatment were significantly different. Moreover, the antibiotic-induced spectral changes in Lb. bulgaricus had good correlation with its proliferation ability and could be potentially used as a base for rapid quantification of the antibiotic susceptibility of bacteria. The developed method could detect the reaction of LAB to antibiotic treatments within 3 h. Therefore, the developed SERS method has the potential to be used to rapidly discriminate and quantify the antibiotic susceptibility of LAB. Moreover, such a rapid and direct method could contribute to microbial antibiotic susceptibility detection research.

Very preterm (VPT) infants are at high-risk for neurodevelopmental impairments, however there are few validated biomarkers at term-equivalent age that accurately measure abnormal brain development and predict future impairments. Our objectives were to quantify and contrast cortical features between full-term and VPT infants at term and to associate two key antecedent risk factors, bronchopulmonary dysplasia (BPD) and retinopathy of prematurity (ROP), with cortical maturational changes in VPT infants. We prospectively enrolled a population-based cohort of 110 VPT infants (gestational age ≤31 weeks) and 51 healthy full-term infants (gestational age 38–42 weeks). Structural brain MRI was performed at term. 94 VPT infants and 46 full-term infants with high-quality T2-weighted MRI were analyzed. As compared to full-term infants, VPT infants exhibited significant global cortical maturational abnormalities, including reduced surface area (−5.9%) and gyrification (−6.7%) and increased curvature (5.9%). In multivariable regression controlled for important covariates, BPD was significantly negatively correlated with lobar and global cortical surface area and ROP was significantly negatively correlated with lobar and global sulcal depth in VPT infants. Our cohort of VPT infants exhibited widespread cortical maturation abnormalities by term-equivalent age that were in part anteceded by two of the most potent neonatal diseases, BPD and ROP.

Shiga toxin-producing Escherichia coli (STEC) is one of the primary pathogenic contaminants of foods, contributing to several foodborne outbreaks in recent years. Here, we report the complete genome sequences of two non-O157 STEC strains isolated from an outbreak of diarrhea in the city of Guilin, Guangxi Zhuang Autonomous Region, China.

Prenatal opioid exposure has been linked to altered neurodevelopment and visual problems such as strabismus and nystagmus. The neural substrate underlying these alterations is unclear. Resting-state functional connectivity MRI (rsfMRI) is an advanced and well-established technique to evaluate brain networks. Few studies have examined the effects of prenatal opioid exposure on resting-state network connectivity in infancy. In this pilot study, we characterized network connectivity in opioid-exposed infants (n = 19) and controls (n = 20) between 4-8 weeks of age using both a whole-brain connectomic approach and a seed-based approach. Prenatal opioid exposure was associated with differences in distribution of betweenness centrality and connection length, with positive connections unique to each group significantly longer than common connections. The unique connections in the opioid-exposed group were more often inter-network connections while unique connections in controls and connections common to both groups were more often intra-network. The opioid-exposed group had smaller network volumes particularly in the primary visual network, but similar network strength as controls. Network topologies as determined by dice similarity index were different between groups, particularly in visual and executive control networks. These results may provide insight into the neural basis for the developmental and visual problems associated with prenatal opioid exposure.

Oral squamous cell carcinoma (OSCC) is the most common malignant tumor in the head and neck, and radiotherapy is the main approach for this disease, while irradiation resistance is a huge challenge that influences radiosensitivity. This study aims to determine the role and function of miR-29a-3p and ADAM12 in the radiosensitivity of OSCC cells. The expression pattern of ADAM12 in OSCC cells was searched in TCGA database. The binding of miR-29a-3p and ADAM12 was predicted by Starbase and verified using dual luciferase reporter gene assay. The RNA or protein expressions of miR-29a-3p and ADAM12 were measured by RT-qPCR or western blot. OSCC cell lines were treated by various γ-ray irradiation dosages before the alteration on miR-29a-3p expression and on the cell viability, proliferation, migration and cell apoptosis was detected. ADAM12 was highly expressed in OSCC cells, whose expression in resistant cells was positively correlated with irradiation dosage. Overexpression of ADAM12 in OSCC cells lead to increased cell proliferation and migration ability as well as inhibited cell apoptosis. miRNAs potentially binding ADAM12 in PITA, microT, miRmap and targetscan were screened, among which miR-29a-3p had the maximum differential expression levels in OSCC cells determined by RT-qPCR. Overexpression of miR-29a-3p resulted in suppressed cell viability, proliferation, migration ability and increased cell apoptosis, while this expression pattern can be partially counteracted by ADAM12 overexpression in OSCC cells. miR-29a-3p through targeting and inhibiting AMDM12 enhances the radiosensitivity of OSCC cells.

The prevalence of disabled survivors of prematurity has increased dramatically in the past 3 decades. These survivors, especially, very preterm infants (VPIs), born ≤ 32 weeks gestational age, are at high risk for neurodevelopmental impairments. Early and clinically effective personalized prediction of outcomes, which forms the basis for early treatment decisions, is urgently needed during the peak neuroplasticity window—the first couple of years after birth—for at-risk infants, when intervention is likely to be most effective. Advances in MRI enable the noninvasive visualization of infants' brains through acquired multimodal images, which are more informative than unimodal MRI data by providing complementary/supplementary depicting of brain tissue characteristics and pathology. Thus, analyzing quantitative multimodal MRI features affords unique opportunities to study early postnatal brain development and neurodevelopmental outcome prediction in VPIs. In this study, we investigated the predictive power of multimodal MRI data, including T2-weighted anatomical MRI, diffusion tensor imaging, resting-state functional MRI, and clinical data for the prediction of neurodevelopmental deficits. We hypothesize that integrating multimodal MRI and clinical data improves the prediction over using each individual data modality. Employing the aforementioned multimodal data, we proposed novel end-to-end deep multimodal models to predict neurodevelopmental (i.e., cognitive, language, and motor) deficits independently at 2 years corrected age. We found that the proposed models can predict cognitive, language, and motor deficits at 2 years corrected age with an accuracy of 88.4, 87.2, and 86.7%, respectively, significantly better than using individual data modalities. This current study can be considered as proof-of-concept. A larger study with external validation is important to validate our approach to further assess its clinical utility and overall generalizability.

The presence of Salmonella in natural freshwater and drinking water is a leading cause of intestinal illness all over the world; thus, the detection of Salmonella in water is of great importance to public health. The objective of this study is to develop a rapid screening method for the detection of Salmonella enterica serovar Enteritidis in water involving surface-enhanced Raman spectroscopy (SERS), aptamers, and filtration. SERS offers a great alternative to traditional methods of pathogen detection, with a simplified detection assay and shortened detection time. The specific capturing and labeling of Salmonella Enteritidis are realized by a specific single-stranded DNA aptamer, which is modified with an additional chain of adenine and fluorescein (FAM) and used as presence/absence indicator of Salmonella Enteritidis. By incorporating a vacuum filtration system, bacterial cells recognized by the specific aptamer are concentrated onto a membrane. With additional filtration of gold nanoparticles, the aptamer signals were captured and used to construct a SERS mapping indicating the presence and absence of target bacterial strains with potential quantitative capability. The specificity of the method was validated by using other strains of bacteria such as Escherichia coli and Listeria monocytogenes. The sensitivity of the method goes down to 103 CFU/mL for 1 mL of sample with a total detection and analyzing time within 3 h. This study demonstrates the capability of the filtration-based SERS platform for detecting Salmonella Enteritidis in various aqueous matrices such as distilled water and rinsing water from fresh produce with high selectivity and sensitivity.

OBJECTIVE. The purpose of this study is to develop a machine learning model to categorically classify MR elastography (MRE)-derived liver stiffness using clinical and nonelastographic MRI radiomic features in pediatric and young adult patients with known or suspected liver disease. MATERIALS AND METHODS. Clinical data (27 demographic, anthropomorphic, medical history, and laboratory features), MRI presence of liver fat and chemical shift-encoded fat fraction, and MRE mean liver stiffness measurements were retrieved from electronic medical records. MRI radiomic data (105 features) were extracted from T2-weighted fast spin-echo images. Patients were categorized by mean liver stiffness (< 3 vs ≥ 3 kPa). Support vector machine (SVM) models were used to perform two-class classification using clinical features, radiomic features, and both clinical and radiomic features. Our proposed model was internally evaluated in 225 patients (mean age, 14.1 years) and externally evaluated in an independent cohort of 84 patients (mean age, 13.7 years). Diagnostic performance was assessed using ROC AUC values. RESULTS. In our internal cross-validation model, the combination of clinical and radiomic features produced the best performance (AUC = 0.84), compared with clinical (AUC = 0.77) or radiomic (AUC = 0.70) features alone. Using both clinical and radiomic features, the SVM model was able to correctly classify patients with accuracy of 81.8%, sensitivity of 72.2%, and specificity of 87.0%. In our external validation experiment, this SVM model achieved an accuracy of 75.0%, sensitivity of 63.6%, specificity of 82.4%, and AUC of 0.80. CONCLUSION. An SVM learning model incorporating clinical and T2-weighted radiomic features has fair-to-good diagnostic performance for categorically classifying liver stiffness.

Objective The accuracy of structural magnetic resonance imaging (MRI) to predict later cerebral palsy (CP) in newborns with perinatal brain injury is variable. Diffusion tensor imaging (DTI) and task-based functional MRI (fMRI) show promise as predictive tools. We hypothesized that infants who later developed CP would have reduced structural and functional connectivity as compared with those without CP. Study Design We performed DTI and fMRI using a passive motor task at 40 to 48 weeks' postmenstrual age in 12 infants with perinatal brain injury. CP was diagnosed at age 2 using a standardized examination. Results Five infants had CP at 2 years of age, and seven did not have CP. Tract-based spatial statistics showed a widespread reduction of fractional anisotropy (FA) in almost all white matter tracts in the CP group. Using the median FA value in the corticospinal tracts as a cutoff, FA was 100% sensitive and 86% specific to predict CP compared with a sensitivity of 60 to 80% and a specificity of 71% for structural MRI. During fMRI, the CP group had reduced functional connectivity from the right supplemental motor area as compared with the non-CP group. Conclusion DTI and fMRI obtained soon after birth are potential biomarkers to predict CP in newborns with perinatal brain injury.

The proliferation of silver nanoparticles (AgNPs) in the environment and resultant transport into aquatic systems have raised concerns regarding their potential toxicity to various organisms. These environmental and ecological concerns demand reliable AgNP detection methods which can measure environmentally relevant quantities of AgNPs in real aquatic systems. This study developed a method that couples a rapid vacuum filtration technique with a portable Raman spectrometer to achieve on-site detection of ultra-low levels of AgNPs in typical and complex aquatic systems. To extract and detect AgNPs, aluminum chloride and ferbam were added for AgNP aggregation and labelling, respectively. The AgNP aggregates were filtered through a membrane, and their presence and quantity were determined based upon the surface-enhanced Raman scattering (SERS) peak intensity of ferbam. Under the optimized conditions, the extraction efficiencies are 99 ± 0.001% in ultrapure water and 98 ± 0.025% in marine water for 1 mg/L AgNPs. This method enables simple volume adjustment and improves the consistency of AgNP distribution on the membrane. The performance of the method was evaluated in different environmental waters, including marine water, fresh waters (pond water, river water, and reservoir outlet water) and drinking waters (municipal tap water and well water), with highest signal intensity in marine water and lowest signals in fresh waters. The signal intensity difference was suggested to be caused by the amount of natural organic matter (NOM) in these environmental waters. Using pond water as an example, the interference was minimized by changing the aggregating salt from AlCl3 to MgCl2, and AgNPs as low as 5 μg/L were reliably detected with a volume of 100 mL. At the same volume, the developed method was sensitive enough to detect 1 μg/L AgNPs in marine water and also holds promise for assessing the time-dependent transformation of AgNPs.

Natural formation of metal nanoparticles is an important pathway that will modify the fate, behavior, and biological availability of heavy metal ions in the environment. Most work has focused on the ability of natural organic matter (NOM) and extracellular polymeric substances (EPS) to convert metal ions into nanoparticles. However, plant roots, ubiquitous in soil and aquatic environments, may have a significant role in the formation of naturally occurring metal nanoparticles. This work demonstrates the importance of plant roots and associated exudates in mediating the transformation of Ag+ in the presence of sunlight. Using Ag+ as the starting material, transformation took place in three steps: 1) formation of AgCl microcubes (μAgCl) through complexation of Ag+ by plant-released chloride ions in root exudates; 2) stabilization of μAgCl by biomolecules in root exudates; 3) partial photoreduction of μAgCl to Ag(0) nanoparticles (nAg) facilitated by exudate biomolecules. Morphological and compositional changes were observed by scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) on the particles from 0–24 h: cubic AgCl microcrystals were converted into cauliflower-shaped core–shell structures with nAg clusters as the shell and μAgCl as the core. The quantification of Ag+, μAgCl and nAg species over time demonstrates that the transformation kinetics fit (R2 = 0.99) a second-order reaction (k = 1.11 mM−1 h−1). The discovery of plant root exudate-mediated phototransformation of Ag+ adds new knowledge to our understanding of Ag transformation in the plant root zone and will guide the assessment of both exposure and risk in the environment.

Mesenchymal stem cells (MSCs) are multipotent stem cells with the capacity to differentiate into several cell types under appropriate conditions. They also possess remarkable antitumor features that make them a novel choice to treat cancers. Accumulating evidence suggest that the MSCs-derived extracellular vesicles, known as exosomes, play an essential role in the therapeutic effects of MSCs mainly by carrying biologically active factors. However, limitations such as low yield of exosomes and difficulty in isolation and purification hinder their clinical applications. To overcome these issues, research on development of exosome-mimics has attracted great attention. This systematic review represents, to the best of our knowledge, the first thorough evaluations of the innate antineoplastic features of MSCs-derived exosomes or exosome-mimics, the methods of drug loading, application as drug delivery system and their impacts on targeted cancer therapy. Importantly, we dissect the commonalities and differences as well as address the shortcomings of work accumulated over the last two decades and discuss how this information can serve as a guide map for optimal experimental design implementation ultimately aiding the effective transition into clinical trials.

Antibiotic resistant bacteria pose an increasing threat to global public health, and it is essential that effective detection methods for identifying these organisms. This study assesses the ability of three different analytical approaches that were developed using surface-enhanced Raman spectroscopy (SERS) to differentiate between antibiotic sensitive and resistant bacteria based on their responses to ampicillin exposure, using Escherichia coli O157:H7 as a model bacterium. The approaches tested in this study included a conventional SERS approach of mixing a droplet of bacterial culture with gold nanoparticles, extracellular matrix analysis, and in situ mapping of bacterial cells on a filter membrane. All three of the SERS techniques were able to differentiate between the sensitive and resistant bacterial strains based on peak intensity changes associated with compounds released by the bacteria in response to antibiotic exposure, with extracellular matrix analysis and filter mapping both observed to be more effective than the conventional approach. However, there were significant differences between the spectra obtained from the different techniques and the potential advantages and disadvantages of each approach should be considered when used in the future. This study shows that SERS can be an effective technique for rapid and efficient assessment of ampicillin sensitivity in E. coli, and more work should be done to explore these analytical approaches with other types of bacterial samples.

Magnetic resonance enterography (MRE) and ultrasound (US) can be used to diagnose inflammatory bowel diseases (IBD) in children. This meta-analysis aimed to determine the diagnostic performance of MRE and US in pediatric patients with IBD. PubMed, Embase, and the Cochrane Library were searched for eligible studies published up to June 1, 2020. The outcomes were the performances of MRE and US at the segment and patient levels. Pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic curves value (SROC) were analyzed. Eight studies (340 children) were included. Compared with the reference standard, MRE showed pooled sensitivity of 93.0% (95% confidence interval (CI): 90.0–95.4%), specificity of 94.6% (95% CI: 92.1–96.5%), PLR of 11.146 (95% CI: 5.027–24.713), NLR of 0.094 (95% CI: 0.057–0.155), and DOR of 134.21 (95% CI: 40.72–442.29), with a SROC of 0.9721. Similar results were observed at the patient and segment levels. Compared with the reference standard, US had pooled sensitivity of 84.1% (95% CI: 69.9–93.4%), specificity of 82.9% (95% CI: 66.4–93.4%), PLR of 4.924 (95% CI: 2.351–10.310), NLR of 0.207 (95% CI: 0.103–0.413), and DOR of 25.919 (95% CI: 7.63–88.07), but only two studies were included. US (reader 1) had a similar diagnostic value to US (reader 2). The present meta-analysis shows that MRE has good performance in detecting IBD in pediatric patients. Only two studies used US, and additional studies are necessary to confirm the diagnostic performance of US for IBD in children. • MRE has good performance in the detection of IBD in pediatric patients. • Similar results were observed at the patient and segment levels for MRE. • Only two studies were included for US, without differentiating patient/segment.

Cancer vaccines represent among the most promising strategies in the battle against cancers. However, the clinical efficacy of current cancer vaccines is largely limited by the lack of optimized delivery systems to generate strong and persistent antitumor immune responses. Moreover, most cancer vaccines require multiple injections to boost the immune responses, leading to poor patient compliance. Controlled-release drug delivery systems are able to address these issues by presenting drugs in a controlled spatiotemporal manner, which allows co-delivery of multiple drugs, reduction of dosing frequency and avoidance of significant systemic toxicities. In this review, we outline the recent progress in cancer vaccines including subunit vaccines, genetic vaccines, dendritic cell-based vaccines, tumor cell-based vaccines and in situ vaccines. Furthermore, we highlight the efforts and challenges of controlled or sustained release drug delivery systems (e.g., microparticles, scaffolds, injectable gels, and microneedles) in ameliorating the safety, effectiveness and operability of cancer vaccines. Finally, we briefly discuss the correlations of vaccine release kinetics and the immune responses to enlighten the rational design of the next-generation platforms for cancer therapy.

Highlights • Most preterm infants exhibit regions of high signal intensity on T2 MRI at term. • Debate remains as to whether this signal (DWMA) is pathological. • We quantified DWMA and used graph theory to measure brain network efficiency. • Whole-brain and regional network efficiency at term decreased with greater DWMA. • DWMA in very preterm infants is associated with reduced brain efficiency at term., Between 50 and 80% of very preterm infants (

Background. Owing to the intrinsic stability against common β-lactamases and metallo-lactamases, monobactams gathered special attention in antibiotic drug development. However, so far, aztreonam is the only monobactam approved by FDA for clinical use. We designed new derivatives of aztreonam to enhance its antibacterial efficacy. Methods. We synthesized a series of monocyclic β-lactams by modifying mainly at the C3 position of azetidinone ring. NH2 group at C3 of azetidinone was attached to thiazole and thiadiazole which in turn was linked to nitrogenous heterocyclic rings via amidine moieties. We then investigated the in vitro antibacterial activities of synthesized compounds against ten bacterial strains of clinical interest in comparison to aztreonam and ceftazidime. Results. All compounds showed improved antibacterial activities against tested strains compared to reference drugs. Compounds 14d and 14e were most potent and showed the highest potency against all bacterial strains, with MIC values ranging from 0.25 µg/mL to 8 µg/mL, as compared to aztreonam (MIC 16 µg/mL to >64 µg/mL) and ceftazidime (MIC >64 µg/mL). These compounds (14d and 14e) may be valuable lead targets against multidrug-resistant Gram-negative bacteria.

Taxillus chinensis (DC.) Danser, a parasitic plant that belongs to the Loranthaceae family, has a long history of being used in the Chinese medicine. We observed that the loranthus seeds were sensitive to temperature and could lose viability below 0°C quickly. Thus, we performed small RNA sequencing to study the microRNA (miRNA) regulation in the loranthus seeds under cold stress. In total, we identified 600 miRNAs, for the first time, in the loranthus seeds under cold stress. Then, we detected 224, 229, and 223 miRNAs ( TPM > 1 ) in A0 (control), A1 (cold treatment for 12 h at 0°C), and A2 (cold treatment for 36 h at 0°C), respectively. We next identified 103 differentially expressed miRNAs (DEmiRs) in the loranthus seeds in response to cold. Notably, miR408 was upregulated during the cold treatment, which can regulate genes encoding phytocyanin family proteins and phytophenol oxidases. Some DEmiRs were specific to A1 and may function in early response to cold, such as gma-miR393b-3p, miR946, ath-miR779.2-3p, miR398, and miR9662. It is interesting that ICE3, IAA13, and multiple transcription factors (e.g., WRKY and CRF4 and TCP4) regulated by the DEmiRs have been reported to respond cold in other plants. We further identified 4, 3, and 4 DEmiRs involved in the pathways “responding to cold,” “responding to abiotic stimulus,” and “seed development/germination,” respectively. qRT-PCR was used to confirm the expression changes of DEmiRs and their targets in the loranthus seeds during the cold treatment. This is the first time to study cold-responsive miRNAs in loranthus, and our findings provide a valuable resource for future studies.

The diazabicyclooctane (DBO) scaffold is the backbone of non-β-lactam-based second generation β-lactamase inhibitors. As part of our efforts, we have synthesized a series of DBO derivatives A1–23 containing amidine substituents at the C2 position of the bicyclic ring. These compounds, alone and in combination with meropenem, were tested against ten bacterial strains for their antibacterial activity in vitro. All compounds did not show antibacterial activity when tested alone (MIC >64 mg/L), however, they exhibited a moderate inhibition activity in the presence of meropenem by lowering its MIC values. The compound A12 proved most potent among the other counterparts against all bacterial species with MIC from E. coli strains with a MIC value of

ImportanceDiffuse white matter abnormality (DWMA) is the most common brain MRI finding in very preterm infants and is predictive of neurodevelopmental impairments. However, its etiology remains elusive and identifying perinatal risk and protective factors may allow clinicians to reduce the burden of DWMA.ObjectiveTo identify perinatal clinical factors that are associated with the development of objectively diagnosed DWMA in very preterm infants.DesignA prospective cohort was enrolled between September 2016 and November 2019. Brain MRIs were collected at 39 to 45 weeks postmenstrual age (PMA) to evaluate DWMA volume. A pre-defined list of pertinent maternal characteristics, pregnancy/delivery data, and neonatal ICU data was collected for enrolled patients to identify antecedents of objectively diagnosed DWMA.SettingFive level III/IV NICUs in the greater Cincinnati, Ohio area.ParticipantsA population-based sample of 392 very preterm infants born before 33 weeks gestational age.ExposureVery preterm birth with associated diseases and treatments.Main Outcome and MeasureObjectively diagnosed DWMA volume on brain MRI at term-equivalent age.Results377 of the 392 very preterm infants (96%) had high quality MRI data. Mean (SD) gestational age was 29.3 (2.5) weeks. In multivariable linear regression analyses, pneumothorax (p=.027), severe bronchopulmonary dysplasia (BPD) (p=.009), severe retinopathy of prematurity (ROP) (pConclusions and RelevanceSevere ROP and BPD exhibited the strongest adverse association with the development of DWMA. Caffeine and dexamethasone treatments for infants with severe BPD exhibited a protective effect against development of DWMA. The beneficial association with maternal milk is also a modifiable factor that has clinical implications.Key PointsQuestionWhat perinatal clinical factors are independently associated with the development of diffuse white matter abnormality (DWMA) in very preterm infants?FindingsIn this prospective cohort study, pneumothorax, severe bronchopulmonary dysplasia (BPD), severe retinopathy of prematurity, and male sex were significantly associated with an increased risk of DWMA. Significant protective factors included dexamethasone for severe BPD, duration of caffeine for severe BPD, and exclusive maternal milk diet at NICU discharge.MeaningKnowledge of these common and modifiable neonatal diseases and treatments may allow clinicians to reduce the burden of DWMA development through targeted prevention or treatments approaches.

Up to 40% of very preterm infants (≤32 weeks’ gestational age) were identified with a cognitive deficit at 2 years of age. Yet, accurate clinical diagnosis of cognitive deficit cannot be made until early childhood around 3–5 years of age. Recently, brain structural connectome that was constructed by advanced diffusion tensor imaging (DTI) technique has been playing an important role in understanding human cognitive functions. However, available annotated neuroimaging datasets with clinical and outcome information are usually limited and expensive to enlarge in the very preterm infants’ studies. These challenges hinder the development of neonatal prognostic tools for early prediction of cognitive deficit in very preterm infants. In this study, we considered the brain structural connectome as a 2D image and applied established deep convolutional neural networks to learn the spatial and topological information of the brain connectome. Furthermore, the transfer learning technique was utilized to mitigate the issue of insufficient training data. As such, we developed a transfer learning enhanced convolutional neural network (TL-CNN) model for early prediction of cognitive assessment at 2 years of age in very preterm infants using brain structural connectome. A total of 110 very preterm infants were enrolled in this work. Brain structural connectome was constructed using DTI images scanned at term-equivalent age. Bayley III cognitive assessments were conducted at 2 years of corrected age. We applied the proposed model to both cognitive deficit classification and continuous cognitive score prediction tasks. The results demonstrated that TL-CNN achieved improved performance compared to multiple peer models. Finally, we identified the brain regions most discriminative to the cognitive deficit. The results suggest that deep learning models may facilitate early prediction of later neurodevelopmental outcomes in very preterm infants at term-equivalent age.

Several recent studies demonstrated that lung ultrasound could achieve desired diagnostic accuracy for transient tachypnea of the neonate (TTN). However, the diagnostic performance of lung ultrasound for TTN has not been systematically studied to date. This meta-analysis aimed to investigate the performance of lung ultrasound in diagnosing TTN. The relevant literature was searched in PubMed, Medline, the Cochrane Library, and Embase databases without any restriction in terms of language and time until January 31, 2021. Studies that assessed the diagnostic performance of lung ultrasound for TTN were included. Seven studies with 1514 participants were summarized. The lung ultrasound provided more accurate performance for diagnosing TTN with pooled sensitivity and specificity of 0.67 [95% confidence interval (CI) = 0.63–0.71] and 0.97 (95% CI = 0.95–0.98), respectively. A higher summarized area under the summary receiver operating characteristic curve was observed as 0.9906. Lower sensitivity and area under the curve (AUC) of B-lines for TTN were observed as 0.330 (95% CI = 0.27–0.38) and 0.5000, respectively. Lung ultrasound provided highly accurate AUC, sensitivity, and specificity in detecting TTN. Large-scale studies are warranted in the future to confirm these results.

Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for neurodevelopmental impairments. Recent advances in deep learning techniques have made it possible to aid the early diagnosis and prognosis of neurodevelopmental deficits. Deep learning models typically require training on large datasets, and unfortunately, large neuroimaging datasets with clinical outcome annotations are typically limited, especially in neonates. Transfer learning represents an important step to solve the fundamental problem of insufficient training data in deep learning. In this work, we developed a multi-task, multi-stage deep transfer learning framework using the fusion of brain connectome and clinical data for early joint prediction of multiple abnormal neurodevelopmental (cognitive, language and motor) outcomes at 2 years corrected age in very preterm infants. The proposed framework maximizes the value of both available annotated and non-annotated data in model training by performing both supervised and unsupervised learning. We first pre-trained a deep neural network prototype in a supervised fashion using 884 older children and adult subjects, and then re-trained this prototype using 291 neonatal subjects without supervision. Finally, we fine-tuned and validated the pre-trained model using 33 preterm infants. Our proposed model identified very preterm infants at high-risk for cognitive, language, and motor deficits at 2 years corrected age with an area under the receiver operating characteristic curve of 0.86, 0.66 and 0.84, respectively. Employing such a deep learning model, once externally validated, may facilitate risk stratification at term-equivalent age for early identification of long-term neurodevelopmental deficits and targeted early interventions to improve clinical outcomes in very preterm infants.

Reducing Magnetic Resonance (MR) image acquisition time can potentially help to reduce cost and make the MRI based assessment more accessible for the patients care. The GRAPPA and Compressive Sensing (CS) have been applied for image reconstruction from the data where original sampled at a rate inferior to the Nyquist-Shannon sample theory. However, most of the existing techniques are iterative and time-consuming, as a result these methods are not suitable for the real-time applications for MRI reconstruction problem. Nowadays, the Deep Learning (DL) based methods have been showing state-of-the-art performance for different image acquisition and reconstruction tasks including MRI reconstruction. The DL based solution is more robust, generic, transferable, and faster. Hence, the deep learning-based solution can be used to deploy a real-time and faster MRI reconstruction system. In this work, we applied the NABLA-N deep learning model for the MRI reconstruction in both image-domain and frequency-domain (k-space). For evaluating the model, the experiments have conducted on publicly available MRI reconstruction challenge dataset. The results demonstrate that the NABLA-N Net (∇N-Net) model is capable to reconstruct high-quality MR images and show that kspace reconstruction model is better and more capable compared to image-space based reconstruction model.

Our objective was to evaluate the independent prognostic value of a novel MRI biomarker – objectively diagnosed diffuse white matter abnormality volume (DWMA; diffuse excessive high signal intensity) – for prediction of motor outcomes in very preterm infants. We prospectively enrolled a geographically-based cohort of very preterm infants without severe brain injury and born before 32 weeks gestational age. Structural brain MRI was obtained at term-equivalent age and DWMA volume was objectively quantified using a published validated algorithm. These results were compared with visually classified DWMA. We used multivariable linear regression to assess the value of DWMA volume, independent of known predictors, to predict motor development as assessed using the Bayley Scales of Infant & Toddler Development, Third Edition at 3 years of age. The mean (SD) gestational age of the cohort was 28.3 (2.4) weeks. In multivariable analyses, controlling for gestational age, sex, and abnormality on structural MRI,DWMA volume was an independent prognostic biomarker of Bayley Motor scores (β= –12.59 [95% CI: –18.70, –6.48] R2=0.41). Conversely, visually classified DWMA was not predictive of motor development. In conclusion, objectively quantified DWMA is an independent prognostic biomarker of long-term motor development in very preterm infants and warrants further study.

About 50%–80% of very preterm infants (VPIs) (≤ 32 weeks gestational age) exhibit diffuse white matter abnormality (DWMA) on their MR images at term-equivalent age. It remains unknown if DWMA is associated with developmental impairments, and further study is warranted. To aid in the assessment of DWMA, a deep learning model for DWMA quantification on T2-weighted MR images was developed. This secondary analysis of prospective data was performed with an internal cohort of 98 VPIs (data collected from December 2014 to April 2016) and an external cohort of 28 VPIs (data collected from January 2012 to August 2014) who had already undergone MRI at term-equivalent age. Ground truth DWMA regions were manually annotated by two human experts with the guidance of a prior published semiautomated algorithm. In a twofold cross-validation experiment using the internal cohort of 98 infants, the three-dimensional (3D) ResU-Net model accurately segmented DWMA with a Dice similarity coefficient of 0.907 ± 0.041 (standard deviation) and balanced accuracy of 96.0% ± 2.1, outperforming multiple peer deep learning models. The 3D ResU-Net model that was trained with the whole internal cohort (n = 98) was further tested on an independent external test cohort (n = 28) and achieved a Dice similarity coefficient of 0.877 ± 0.059 and balanced accuracy of 92.3% ± 3.9. The externally validated 3D ResU-Net deep learning model for accurately segmenting DWMA may facilitate the clinical diagnosis of DWMA in VPIs. Supplemental material is available for this article. Keywords: Brain/Brain Stem, Convolutional Neural Network (CNN), MR-Imaging, Pediatrics, Segmentation, Supervised learning © RSNA, 2021